Finals Week Concentrates Medal Events Into Evening Hours

Comparing opening days vs. closing days of the Milan-Cortina 2026 Olympics

Comparing opening versus closing days of the Milan-Cortina 2026 Winter Olympics reveals how finals week dramatically concentrates medal events into evening hours, transforming the schedule from scattered daytime competition to evening-focused spectacle.

Figure 1: Grouped bar chart comparing medal event share by hour between Opening Days (gray bars) and Finals Week (teal bars) of the 2026 Winter Olympics. Finals Week shows dramatically higher medal event concentration in evening hours, with 4pm, 5pm, and 10pm reaching 100% medal events, while Opening Days shows lower, more scattered distribution throughout the day.

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse, ggtext, showtext, janitor, 
    scales, glue
)
})

### |- figure size ----
camcorder::gg_record(
  dir    = here::here("temp_plots"),
  device = "png",
  width  = 10,
  height = 7,
  units  = "in",
  dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

# tt <- tidytuesdayR::tt_load(2026, week = 06)
# schedule <- tt$schedule |> clean_names()
# rm(tt)
schedule <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2026/2026-02-10/schedule.csv')
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(schedule)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

### |-  data cleaning ----
schedule_clean <- schedule |>
  mutate(
    olympic_day = as.numeric(date - min(date)) + 1,
    max_day = max(olympic_day),
    hour_local = hour(start_datetime_local),

    # Two periods for direct comparison
    period = case_when(
      olympic_day <= 3 ~ "Opening Days (1-3)",
      olympic_day >= max_day - 2 ~ "Finals Week",
      TRUE ~ NA_character_
    )
  ) |>
  filter(!is.na(period)) |>
  mutate(period = fct_relevel(period, "Opening Days (1-3)", "Finals Week"))

### |-  calculate medal event share ----
medal_share_comparison <- schedule_clean |>
  count(period, hour_local, is_medal_event) |>
  group_by(period, hour_local) |>
  mutate(
    total_events = sum(n),
    pct = n / total_events * 100
  ) |>
  ungroup() |>
  filter(is_medal_event == TRUE)
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = list(
      col_opening = "#CFCFCF",      
      col_closing = "#0d9488",       
      col_primetime_shade = "#ccfbf1",
      col_text_dark = "gray30"
  )
)

### |- titles and caption ----
title_text <- "Finals Week Concentrates Medal Events Into Evening Hours"

subtitle_text <- "Comparing opening days vs. closing days of the Milan-Cortina 2026 Olympics"

caption_text <- create_social_caption(
    tt_year = 2026,
    tt_week = 06,
    source_text = "2026 Winter Olympics Schedule (Milan-Cortina)"
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    # Text styling
    plot.title = element_text(
      face = "bold", family = fonts$title, size = rel(1.3),
      color = colors$title, margin = margin(b = 10), hjust = 0
    ),
    plot.subtitle = element_markdown(
      face = "italic", family = fonts$subtitle, lineheight = 1.2,
      color = colors$subtitle, size = rel(0.8), margin = margin(b = 20), hjust = 0
    ),

    # Grid
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.major = element_line(color = "gray90", linewidth = 0.25),

    # Axes
    axis.title = element_text(size = rel(0.8), color = "gray30"),
    axis.text = element_text(color = "gray30"),
    axis.text.y = element_text(size = rel(0.85)),
    axis.ticks = element_blank(),

    # Facets
    strip.background = element_rect(fill = "gray95", color = NA),
    strip.text = element_text(
      face = "bold",
      color = "gray20",
      size = rel(0.9),
      margin = margin(t = 6, b = 4)
    ),
    panel.spacing = unit(1.5, "lines"),

    # Legend elements
    legend.position = "plot",
    legend.title = element_text(
      family = fonts$subtitle,
      color = colors$text, size = rel(0.8), face = "bold"
    ),
    legend.text = element_text(
      family = fonts$tsubtitle,
      color = colors$text, size = rel(0.7)
    ),
    legend.margin = margin(t = 15),

    # Plot margin
    plot.margin = margin(10, 20, 10, 20),
    
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- final plot ----
p <- medal_share_comparison |>
  ggplot(aes(x = hour_local, y = pct, fill = period)) +

  # Geoms
  geom_col(
    aes(alpha = period),
    position = position_dodge(width = 0.9),
    width = 0.8
  ) +
  # Annotate
  annotate(
    "rect",
    xmin = 18, xmax = 22,
    ymin = 0, ymax = 100,
    fill = colors$palette$col_primetime_shade,
    alpha = 0.08
  ) +
  annotate(
    "text",
    x = 19.5, y = 90,
    label = "In finals week, evening schedules are\ndominated by medal events",
    family = fonts$text,
    size = 3.5,
    color = colors$palette$col_text_dark,
    lineheight = 1.1,
    hjust = 0.5
  ) +
  # Scales
  scale_fill_manual(
    values = c(
      "Opening Days (1-3)" = colors$palette$col_opening,
      "Finals Week" = colors$palette$col_closing
    )
  ) +
  scale_alpha_manual(
    values = c("Opening Days (1-3)" = 0.7, "Finals Week" = 0.95),
    guide = "none"
  ) +
  scale_x_continuous(
    breaks = seq(10, 22, 2),
    labels = function(x) paste0(x %% 12, ifelse(x < 12, "am", "pm")),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  scale_y_continuous(
    labels = function(x) paste0(x, "%"),
    limits = c(0, 100),
    expand = expansion(mult = c(0, 0.05))
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    x = "Hour of Day (Local Milan-Cortina Time)",
    y = "Share of Scheduled Events\nThat Are Medal Events",
    caption = caption_text
  ) +
  # Theme
  theme(
    axis.title.y = element_text(
      angle = 0,
      vjust = 1.04,
      hjust = 0.5,
      margin = margin(r = -100)
    ),
    plot.title = element_markdown(
      size = rel(1.4),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      lineheight = 1.15,
      margin = margin(t = 0, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.8),
      family = fonts$subtitle,
      color = alpha(colors$subtitle, 0.88),
      lineheight = 1.5,
      margin = margin(t = 5, b = 25)
    ),
    plot.caption = element_markdown(
      size = rel(0.5),
      family = fonts$subtitle,
      color = colors$caption,
      hjust = 0,
      lineheight = 1.4,
      margin = margin(t = 20, b = 5)
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot(
  plot = p, 
  type = "tidytuesday", 
  year = 2026, 
  week = 06, 
  width  = 10,
  height = 7,
  )
```

8. Session Info

Expand for Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      glue_1.8.0      scales_1.3.0    janitor_2.2.0  
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6      xfun_0.49         htmlwidgets_1.6.4 tzdb_0.5.0       
 [5] vctrs_0.6.5       tools_4.4.0       generics_0.1.3    curl_6.0.0       
 [9] parallel_4.4.0    gifski_1.32.0-1   fansi_1.0.6       pkgconfig_2.0.3  
[13] lifecycle_1.0.4   farver_2.1.2      compiler_4.4.0    textshaping_0.4.0
[17] munsell_0.5.1     codetools_0.2-20  snakecase_0.11.1  htmltools_0.5.8.1
[21] yaml_2.3.10       pillar_1.9.0      crayon_1.5.3      camcorder_0.1.0  
[25] magick_2.8.5      commonmark_1.9.2  tidyselect_1.2.1  digest_0.6.37    
[29] stringi_1.8.4     labeling_0.4.3    rsvg_2.6.1        rprojroot_2.0.4  
[33] fastmap_1.2.0     grid_4.4.0        colorspace_2.1-1  cli_3.6.4        
[37] magrittr_2.0.3    utf8_1.2.4        withr_3.0.2       bit64_4.5.2      
[41] timechange_0.3.0  rmarkdown_2.29    bit_4.5.0         ragg_1.3.3       
[45] hms_1.1.3         evaluate_1.0.1    knitr_1.49        markdown_1.13    
[49] rlang_1.1.6       gridtext_0.1.5    Rcpp_1.0.13-1     xml2_1.3.6       
[53] renv_1.0.3        svglite_2.1.3     rstudioapi_0.17.1 vroom_1.6.5      
[57] jsonlite_1.8.9    R6_2.5.1          systemfonts_1.1.0

9. GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_2026_06.qmd.

For the full repository, click here.

10. References

Expand for References

1. Data Source:
   • TidyTuesday 2026 Week 06: 2026 Winter Olympics!

11. Custom Functions Documentation

📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils